Ho Cyrus Su Hui, Wang Jinyuan, Tay Gabrielle Wann Nii, Ho Roger, Lin Hai, Li Zhifei, Chen Nanguang
Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Department of Psychological Medicine, National University Hospital, Singapore, Singapore.
Transl Psychiatry. 2025 Jan 11;15(1):7. doi: 10.1038/s41398-025-03224-7.
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. The task change of total haemoglobin (HbT), defined as the difference between pre-task and post-task average HbT concentrations, in the dorsolateral prefrontal cortex (dlPFC) is significantly correlated with treatment response (p < 0.005). Leveraging a Naïve Bayes model, inner cross-validation performance (bAcc = 70% [SD = 4], AUC = 0.77 [SD = 0.04]) and outer cross-validation results (bAcc = 73% [SD = 3], AUC = 0.77 [SD = 0.02]) were yielded for predicting response using solely fNIRS data. The bimodal model combining fNIRS and clinical data showed inferior performance in outer cross-validation (bAcc = 68%, AUC = 0.70) compared to the fNIRS-only model. Collectively, fNIRS holds potential as a scalable neuroimaging modality for predicting treatment response in MDD.
抑郁症治疗反应在个体间差异很大。识别对治疗结果具有预测准确性的客观生物标志物可以提高治疗效率并避免无效治疗。本研究调查功能近红外光谱（fNIRS）和临床评估信息是否可以通过机器学习技术预测重度抑郁症（MDD）的治疗反应。70例MDD患者纳入了这项为期6个月的纵向研究，主要治疗结果通过汉密尔顿抑郁量表（HAM-D）评分的变化来衡量。使用嵌套交叉验证严格评估fNIRS和临床信息，以基于机器学习模型（包括支持向量机、随机森林、XGBoost、判别分析、朴素贝叶斯和变压器）预测反应者和无反应者。背外侧前额叶皮层（dlPFC）中总血红蛋白（HbT）的任务变化定义为任务前和任务后平均HbT浓度之间的差异，与治疗反应显著相关（p < 0.005）。利用朴素贝叶斯模型，仅使用fNIRS数据预测反应的内部交叉验证性能（bAcc = 70% [标准差 = 4]，AUC = 0.77 [标准差 = 0.04]）和外部交叉验证结果（bAcc = 73% [标准差 = 3]，AUC = 0.77 [标准差 = 0.02]）得以产生。与仅使用fNIRS的模型相比，结合fNIRS和临床数据的双峰模型在外部交叉验证中的性能较差（bAcc = 68%，AUC = 0.70）。总体而言，fNIRS作为一种可扩展的神经成像模式在预测MDD治疗反应方面具有潜力。
